Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Cosegmentation Loss: Enhancing segmentation with a Few Training Samples by Transferring Region Knowledge to Unlabeled Images
Wataru Shimoda, Keiji Yanai
Feb 17, 2017 (modified: Feb 21, 2017)ICLR 2017 workshop submissionreaders: everyone
Abstract:We treat semantic segmentation where a few pixel-wise labeled samples
and a large number of unlabeled samples are available. For this
situation we propose cosegmentation loss which enables us to transfer
the knowledge of a few pixel-wise labeled samples to a large number of
unlabeled images. In the experiments, we used human-part segmentation
with a few pixel-wise labeled images and 1715 unlabeled images, and
proved that the proposed co-segmentation loss helped make effective use
of unlabeled images.
TL;DR:Co-Segmentation Loss for semi-supervised semantic segmentation
Enter your feedback below and we'll get back to you as soon as possible.